针对局部阴影引起的光伏阵列多峰值的输出特性,传统的单峰值MPPT算法已无法适用,为寻得全局最优解,将参数设置简单、易于理解的多元宇宙优化算法原理应用于光伏发电的多峰值MPPT模块中。通过仿真分析,证明了基于多元宇宙优化算法的MPPT控制模型能较快地实现全局最大功率点的跟踪,具有更好的收敛速度和精度。该算法能有效解决光伏电站中多峰值功率点的问题,减少寻优过程的功率损耗,从而提高光电转换率,为实际工程带来重要的经济效益。
Abstract
Aiming at the multi peak output characteristics of photovoltaic array caused by local shadow, the traditional single peak algorithm has been unable to apply. In order to find the global optimal solution, a MPPT control algorithm based on multi universe optimization algorithm is proposed. The principle of multi universe optimization algorithm is easy to understand, and the parameter setting is simple, so it is convenient to apply in the multi peak MPPT module of photovoltaic power generation. Through simulation analysis, it is proved that MPPT control model based on multi universe optimization algorithm can achieve global maximum power point tracking in a short time. The algorithm can be combined with other algorithms to further improve the optimization efficiency of the algorithm, which has important guiding significance for photovoltaic MPPT multi peak optimization algorithm, and also has technical and theoretical reference value for engineering practice.
关键词
太阳电池 /
最大功率跟踪 /
电路仿真 /
多峰 /
多元宇宙优化算法
Key words
solar cells /
maximum power point trackers /
circuit simulation /
multi peak /
multi-verse optimization algorithm
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] KOBAYASHI K, TAKANO I, SAWADA Y.A study of a two stage maximum power point tracking control of a photovoltaic system under partially shaded insolation conditions[J]. Solar energy materials and solar cells, 2006, 90(18/19): 2975-2988.
[2] 王云平, 李颖, 阮新波. 基于局部阴影下光伏阵列电流特性的最大功率点跟踪算法[J]. 电工技术学报, 2016, 31(14): 201-210,218.
WANG Y P, LI Y, RUAN X B.Maximum power point tracking algorithm for photovoltaic array under partial shading based on current property[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 201-210, 218.
[3] 尹立敏, 吕莉莉, 雷钢, 等. 全局MPPT算法在局部阴影下的动态性能研究[J]. 电测与仪表, 2018, 55(11): 52-57.
YIN L M, LYU L L, LEI G, et al.Research on the dynamic performance of global MPPT algorithm under the partial shadow[J]. Electrical measurement & instrumentation, 2018, 55(11): 52-57.
[4] 葛俊杰, 赵争鸣, 袁立强, 等. 基于模拟定位电路的多峰值MPPT方法[J]. 电力自动化设备, 2013, 33(10): 16-21.
GE J J, ZHAO Z M, YUAN L Q, et al.Multi-peak MPPT method based on analog positioning circuit[J]. Electric power automation equipment, 2013, 33(10): 16-21.
[5] 闵轩. 光伏发电系统单峰值与多峰值MPPT算法的研究及并网策略分析[D]. 南昌: 南昌大学, 2018.
MIN X.Research on single-peak and multi-peak MPPT algorithm of photovoltaic power generation system and analysis of its grid-connected strategies[D]. Nanchang: Nanchang University, 2018.
[6] 朱艳伟, 石新春, 但扬清, 等. 粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用[J]. 中国电机工程学报, 2012, 32(4): 42-48, 20.
ZHU Y W, SHI X C, DAN Y Q, et al.Application of PSO algorithm in global MPPT for PV array[J]. Proceedings of the CSEE, 2012, 32(4): 42-48, 20.
[7] 杨洁, 蒋林, 蹇清平, 等. 基于模拟退火粒子群优化的光伏多峰最大功率跟踪算法[J]. 计算机应用, 2014, 34(S1): 330-333.
YANG J, JIANG L, JIAN Q P, et al.Multi-peak maximum power point tracking algorithm based on simulated annealing particle swarm optimization for PV systems[J]. Journal of computer applications, 2014, 34(S1): 330-333.
[8] LI H, YANG D, SU W Z, et al.An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading[J]. IEEE transactions on industrial electronics, 2019, 66(1): 265-275.
[9] 宁勇. 基于粒子群算法的光伏系统控制方法优化研究[D]. 长沙: 湖南大学, 2019.
NING Y.Research on optimization of photovoltaic system control method based on particle swarm optimization[D]. Changsha: Hunan University, 2019.
[10] BESHEER A H, ADLY M.Ant colony system based PI maximum power point tracking for stand alone photovoltaic system[C]//2012 IEEE International Conference on Industrial Technology. Athens, Greece 2012: 693-698.
[11] 商立群, 朱伟伟. 基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法[J]. 电工技术学报, 2019, 34(12): 2606-2614.
SHANG L Q, ZHU W W.Photovoltaic system global maximum power point tracking method based on the global learning adaptive bacteria foraging algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(12): 2606-2614.
[12] 聂晓华, 王薇. 混沌改进猫群算法及其在光伏MPPT中的应用[J]. 中国电机工程学报, 2016, 36(22): 6103-6110.
NIE X H, WANG W.Chaos improved cat swarm optimization and its application in the PV MPPT[J]. Proceedings of the CSEE, 2016, 36(22): 6103-6110.
[13] 吴忠强, 于丹琦, 康晓华. 改进鸡群算法在光伏系统MPPT中的应用[J]. 太阳能学报, 2019, 40(6): 1589-1598.
WU Z Q, YU D Q, KANG X H.Application of improved chicken swarm optimization for MPPT in photovoltaic system[J]. Acta energiae solaris sinica, 2019, 40(6): 1589-1598.
[14] MIRJALILI S, MIRJALILI S M, HATAMLOU A.Multi-Verse optimizer: a nature-inspired algorithm for global optimization[J]. Neural computing and applications, 2016, 27(2): 495-513.
[15] FARIS H, HASSONAH M A, ALZOUBI A M, et al.A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture[J]. Neural computing & applications, 2018, 30(8): 2355-2369.
[16] 张辉, 李志军, 张旭, 等. 基于光伏电站的大数据采集系统[J]. 电测与仪表, 2018, 21(3): 31-34, 58.
ZHANG H, LI Z J, ZHANG X, et al.The photovoltaic power plant data acquisition system[J]. Electrical measurement & instrumentation, 2018, 21(3): 31-34, 58.
[17] 刘秋华, 张秀锦, 樊陈. 基于功率差变步长扰动观察法的MPPT控制算法[J]. 电源技术, 2020, 44(7): 1035-1039.
LIU Q H, ZHANG X J, FAN C.MPPT control algorithm based on power difference variable step-size disturbance observation method[J]. Chinese journal of power sources, 2020, 44(7): 1035-1039.
[18] 潘魏. 多元宇宙优化算法及应用研究[D]. 南宁: 广西民族大学, 2017.
PAN W.The research of multi-verse optimization algorithm and its application[D]. Nanning: Guangxi University for Nationalities, 2017.
基金
国家电网公司科技项目（5108-202055023A-0-0-00）; 江苏省高校哲学社会科学研究重点项目（2018SJZDI097）; 南京工程学院校级科研基金项目（CKJB201907）
PDF(2051 KB)
